Code
#General use
library(tidyverse)
#Visualizations
library(plotly)
library(usmap)
library(ggplot2)
library(kableExtra)
#Model building
library(tidymodels)
library(rpart)
library(yardstick)
library(rpart)
library(baguette)DDHQ Election Fellow Data Report
Overview
The following report outlines the methodology utilized to obtain predictions for the margin of victory between Republican and Democratic candidates in all 2018 house races.
Packages used
Loading and Splitting the Data
Once I loaded the data into R, I split the data set into training and testing sets utilizing a split of 75% and 25%. I chose this proportion due to the decent size of the data set.
Additionally, I performed initial pre-processing steps, the further pre-processing steps are highlighted in its respective section. I initially did the following:
Transformed the victory margin to a numeric form
Excluded the 2018 observations from the training data since I will be predicting the victory margins, hence, there are no victory margin observations in 2018. I did not exclude these observations from the testing data since the model will be evaluated on the testing data.
Divided the victory margin by 100 since it is in percentage form and all of the predictor variables are in decimal form
LOADING IN DATA
Code
options(scipen = 100, digits = 4)
election_results<-read.csv("DDHQ_Data_Exercise-1.csv")
election_results$R.D.Victory.Margin<-as.numeric(election_results$R.D.Victory.Margin)
results<-subset(election_results,Year!="2018")
election_results$R.D.Victory.Margin<-election_results$R.D.Victory.Margin/100SPLITTING DATA
Code
#Splitting data
set.seed(20201020)
result_split<-initial_split(data=election_results,prop=0.75)
#Training data
result_train<-training(x=result_split)
result_train<-subset(result_train,Year!="2018")
#Testing data
result_test<-testing(x=result_split)Exploratory Data Analysis/Visualizations
To gain a more robust understanding of the data provided, in addition to exploring the data manually by doing some auxiliary regressions, I also explored the variance of the data to help inform the type of model I would create, the boxplot indicates high variance of the training data.
VARIANCE OF VICTORY MARGIN OVER TIME
Code
boxplot(R.D.Victory.Margin*100~Year,
data=result_test)
Additionally, I created a visualization of the average vote margin between Democratic and Republican candidates in each state. I aggregated the house districts and found the average margin in each state throughout the time frame of 2006-2016.
The visualization can be seen here in both a stagnant and interactive form:
FINDING AVERAGE HOUSE VICTORY MARGIN PER STATE OVER TIME (2006-2016)
Code
averages<-result_train%>%
group_by(state=State)%>%
summarize(Average_Margin=mean(R.D.Victory.Margin*100))STAGNANT VISUALIZATION
Code
margin_plot<-
plot_usmap(regions="state",
data=averages,
values="Average_Margin",
color="black",
size=0.001)+
scale_fill_gradientn(colors=c("blue","blue","white", "red"))+
labs(title="Average House District Victory Margin By State from 2006-2016")+
theme(legend.position = "right",
panel.background=element_rect(colour = "black", fill = "white"),
plot.title = element_text(face="bold"))
margin_plotCode
margin_plot
INTERACTIVE VISUALIZATION
Code
interactive_plot<-ggplotly(margin_plot)Code
interactive_plotData Pre-Processing
To ensure the data could be used in a predictive model, I performed further data pre-processing. In addition to the initial pre-processing noted earlier (transforming the victory margin variable to be numeric and in decimal form and filtering the 2018 observations from the training data) when loading and splitting the data, I also created a recipe with the pre-processing steps to do the following:
Remove all string predictors: this removed the variables of “Race.ID”, “Chamber,” “State,” “Incumbent.Running.,” and “Geography” from set since the non-numeric variables will not be relevant in obtaining predicted values.
Remove “Congressional District” and “Year” variables: while these are numeric variables, they are not relevant predictors of victory margin.
To apply these steps to the data, I prepped the recipe and then applied it to the training data via baking it.
DATA PRE-PROCESSING
Code
#Creating recipe for pre-processing
margin_recipe<-recipe(R.D.Victory.Margin~.,data=result_train)%>%
step_rm(all_string_predictors())%>%
step_rm("Congressional.District")%>%
step_rm("Year")%>%
prep()
#Baking the recipe
bake(margin_recipe,new_data=result_train)Building and implementing the model
I decided to utilize a decision tree model using bootstrap aggregation.
- In performing exploratory data analysis, I noticed that in the training data, the victory margin has consistently high variance throughout the years. Given the context of the data representing all of the Congressional house districts across the country that have vastly different racial, political, and geographic demographics, this makes sense. Additionally, there are several occurrances of seats being unopposed. To account for the data’s high variance and the unopposed race outliers, and to reduce overfitting of the data, I utilized bootstrap aggregation for resampling and decision trees.
CREATING MODEL
Code
#Model
bag_mod<-
bag_tree()%>%
set_engine("rpart")%>%
set_mode("regression")
#Workflow
bag_wf<-
workflow()%>%
add_model(bag_mod)%>%
add_recipe(margin_recipe)ESTIMATING MODEL
Code
#Fitting model on training data
bag_fit<-bag_wf%>%
fit(result_train)EVALUATING MODEL
Code
#Evaluating model on testing data
margin_predicted<-bind_cols(result_test,
predict(object=bag_fit,
new_data=result_test))
summary(result_test$R.D.Victory.Margin)IMPLEMENTING MODEL
Code
#Implementing model for prediction
implementation_predictions<-bind_cols(election_results,
predict(object=bag_fit,
new_data=election_results))
#Putting victory margin back into percent
implementation_predictions$R.D.Victory.Margin<-implementation_predictions$R.D.Victory.Margin*100
implementation_predictions$.pred<-implementation_predictions$.pred*100
#Filtering implemented data to see results
implementation_predictions<-implementation_predictions%>%select(c("Race.ID", "Year","R.D.Victory.Margin",".pred"))Model Evaluation and Interpretation
Through evaluating the metrics of the model, I found the RMSE to be 9.80, indicating that the predicted values on average, deviated 9.80% from the true victory margin in the races from 2006-2016. Additionally, I analyzed the predictions from 2018 and the model predicted a 243D/192R split of the house.
Following these evaluations, I looked at the actual 2018 results and found that the model was okay, not great, it was not completely accurate in predicting the margins of victory and the results.
This was expected for a multitude of reasons. Predicting the voting pattern of individuals within a house district is incredibly challenging. The voting behavior of individuals depends heavily on various factors outside of what was included in the model. Such considerations may include the candidate running and candidate/voter value alignment. Another major consideration that can be challenging to capture is the impact of redistricting. Redistricting was conducted in 2010 and thus impacted the racial and political makeup of house districts across the country. The change in racial and political composition of districts thus impacted the voting behavior of the district as the whole and as such, also impacted the margin of victory between candidates.
Some ways to account for these important and impactful considerations to improve the model fit and ability to accurately predict victory margins could be through (1) feature engineering to account for the the challenge of capturing voting patterns and (2) adding more predictor variables to make the model more robust (I will note, however, that it can be challenging to gather house district specific demographic data).
METRICS
Code
#Metrics
implementation_predictions %>%
filter(Year!="2018")%>%
metrics(R.D.Victory.Margin, .pred)# A tibble: 3 × 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 rmse standard 9.80
2 rsq standard 0.957
3 mae standard 5.22 PREDICTIONS (SEAT COUNT/MARGINS)
Code
#Filtering to see 2018 results
pred_2018<-filter(implementation_predictions,Year=="2018")%>%select(c("Race.ID", ".pred"))
#Seeing breakdown of results
pred_2018%>%count(grepl("-",.pred)) grepl("-", .pred) n
1 FALSE 243
2 TRUE 192Code
#Seeing 2018 Predictions
pred_2018%>%
kbl(caption="2018 Predicted Victory Margins")%>%
kable_minimal("hover",full_width=F)%>%
scroll_box(width = "300px", height = "200px")| Race.ID | .pred |
|---|---|
| H2018AK00 | 20.9236 |
| H2018AL01 | 30.2545 |
| H2018AL02 | 15.2091 |
| H2018AL03 | 30.0773 |
| H2018AL04 | 55.6655 |
| H2018AL05 | 40.6964 |
| H2018AL06 | 52.9900 |
| H2018AL07 | -100.0000 |
| H2018AR01 | 51.6264 |
| H2018AR02 | 18.0336 |
| H2018AR03 | 43.9236 |
| H2018AR04 | 34.9864 |
| H2018AZ01 | -11.0845 |
| H2018AZ02 | 12.5736 |
| H2018AZ03 | -40.2809 |
| H2018AZ04 | 55.6764 |
| H2018AZ05 | 27.5736 |
| H2018AZ06 | 33.0291 |
| H2018AZ07 | -100.0000 |
| H2018AZ08 | 28.9064 |
| H2018AZ09 | -17.7164 |
| H2018CA01 | 19.7227 |
| H2018CA02 | -51.3282 |
| H2018CA03 | -22.2755 |
| H2018CA04 | 22.7164 |
| H2018CA05 | -100.0000 |
| H2018CA06 | -100.0000 |
| H2018CA07 | -9.6136 |
| H2018CA08 | 100.0000 |
| H2018CA09 | -19.4545 |
| H2018CA10 | 12.2655 |
| H2018CA11 | -39.8064 |
| H2018CA12 | -66.0300 |
| H2018CA13 | -100.0000 |
| H2018CA14 | -57.7873 |
| H2018CA15 | -44.6809 |
| H2018CA16 | -13.8173 |
| H2018CA17 | -49.1445 |
| H2018CA18 | -45.7264 |
| H2018CA19 | -51.3273 |
| H2018CA20 | -100.0000 |
| H2018CA21 | 4.6764 |
| H2018CA22 | 28.0636 |
| H2018CA23 | 27.6809 |
| H2018CA24 | -10.3127 |
| H2018CA25 | 13.1173 |
| H2018CA26 | -26.3700 |
| H2018CA27 | -100.0000 |
| H2018CA28 | -52.7345 |
| H2018CA29 | -63.5136 |
| H2018CA30 | -41.1582 |
| H2018CA31 | -16.9400 |
| H2018CA32 | -45.3464 |
| H2018CA33 | -38.4564 |
| H2018CA34 | -100.0000 |
| H2018CA35 | -41.7973 |
| H2018CA36 | -24.9145 |
| H2018CA37 | -74.7627 |
| H2018CA38 | -45.6936 |
| H2018CA39 | 8.8627 |
| H2018CA40 | -100.0000 |
| H2018CA41 | -34.2800 |
| H2018CA42 | 31.2636 |
| H2018CA43 | -63.4973 |
| H2018CA44 | -100.0000 |
| H2018CA45 | 7.2000 |
| H2018CA46 | -43.8809 |
| H2018CA47 | -28.2564 |
| H2018CA48 | 17.5300 |
| H2018CA49 | 7.3000 |
| H2018CA50 | 29.1382 |
| H2018CA51 | -42.3064 |
| H2018CA52 | -19.9264 |
| H2018CA53 | -36.1309 |
| H2018CO01 | -39.8718 |
| H2018CO02 | -30.0627 |
| H2018CO03 | 6.8427 |
| H2018CO04 | 32.4282 |
| H2018CO05 | 26.1336 |
| H2018CO06 | 5.4027 |
| H2018CO07 | -15.2936 |
| H2018CT01 | -34.0009 |
| H2018CT02 | -17.4545 |
| H2018CT03 | -33.2564 |
| H2018CT04 | -28.3309 |
| H2018CT05 | -7.8427 |
| H2018DE00 | -14.9327 |
| H2018FL01 | 44.5882 |
| H2018FL02 | 36.0400 |
| H2018FL03 | 16.4782 |
| H2018FL04 | 36.4327 |
| H2018FL05 | -24.2255 |
| H2018FL06 | 23.3018 |
| H2018FL07 | 3.7327 |
| H2018FL08 | 22.1118 |
| H2018FL09 | -10.0273 |
| H2018FL10 | -100.0000 |
| H2018FL11 | 26.4118 |
| H2018FL12 | 27.0764 |
| H2018FL13 | -4.8955 |
| H2018FL14 | -100.0000 |
| H2018FL15 | 0.2000 |
| H2018FL16 | 26.5473 |
| H2018FL17 | 27.9300 |
| H2018FL18 | 9.7009 |
| H2018FL19 | 28.8418 |
| H2018FL20 | -100.0000 |
| H2018FL21 | -100.0000 |
| H2018FL22 | -16.2509 |
| H2018FL23 | -22.2845 |
| H2018FL24 | -100.0000 |
| H2018FL25 | 5.3964 |
| H2018FL26 | 6.5473 |
| H2018FL27 | 5.9973 |
| H2018GA01 | 28.8909 |
| H2018GA02 | -18.9164 |
| H2018GA03 | 39.9145 |
| H2018GA04 | -45.6682 |
| H2018GA05 | -100.0000 |
| H2018GA06 | 19.4455 |
| H2018GA07 | 25.3036 |
| H2018GA08 | 100.0000 |
| H2018GA09 | 57.8564 |
| H2018GA10 | 32.7827 |
| H2018GA11 | 23.4236 |
| H2018GA12 | 26.5955 |
| H2018GA13 | -46.7964 |
| H2018GA14 | 59.5400 |
| H2018HI01 | -38.0491 |
| H2018HI02 | -42.8691 |
| H2018IA01 | 20.4527 |
| H2018IA02 | -12.9264 |
| H2018IA03 | 6.8418 |
| H2018IA04 | 27.6300 |
| H2018ID01 | 40.2145 |
| H2018ID02 | 32.9309 |
| H2018IL01 | -47.2936 |
| H2018IL02 | -63.3909 |
| H2018IL03 | -36.2027 |
| H2018IL04 | -64.9518 |
| H2018IL05 | -47.7836 |
| H2018IL06 | 20.0664 |
| H2018IL07 | -74.3591 |
| H2018IL08 | -19.9700 |
| H2018IL09 | -35.5536 |
| H2018IL10 | -11.1827 |
| H2018IL11 | -23.4500 |
| H2018IL12 | 19.3109 |
| H2018IL13 | 1.2745 |
| H2018IL14 | 15.2800 |
| H2018IL15 | 52.9745 |
| H2018IL16 | 24.6636 |
| H2018IL17 | -26.3073 |
| H2018IL18 | 42.9064 |
| H2018IN01 | -42.9909 |
| H2018IN02 | 26.0009 |
| H2018IN03 | 33.0400 |
| H2018IN04 | 30.3045 |
| H2018IN05 | 31.6436 |
| H2018IN06 | 44.7227 |
| H2018IN07 | -20.2800 |
| H2018IN08 | 33.1055 |
| H2018IN09 | 19.8464 |
| H2018KS01 | 41.4791 |
| H2018KS02 | 27.8600 |
| H2018KS03 | 10.8309 |
| H2018KS04 | 22.4845 |
| H2018KY01 | 52.6691 |
| H2018KY02 | 48.3364 |
| H2018KY03 | -19.6709 |
| H2018KY04 | 40.9318 |
| H2018KY05 | 58.0264 |
| H2018KY06 | 23.2609 |
| H2018LA01 | 56.8245 |
| H2018LA02 | -100.0000 |
| H2018LA03 | 50.2909 |
| H2018LA04 | 24.7491 |
| H2018LA05 | 26.5182 |
| H2018LA06 | 34.8555 |
| H2018MA01 | -100.0000 |
| H2018MA02 | -34.9745 |
| H2018MA03 | -30.7973 |
| H2018MA04 | -100.0000 |
| H2018MA05 | -40.7000 |
| H2018MA06 | -34.4118 |
| H2018MA07 | -100.0000 |
| H2018MA08 | -100.0000 |
| H2018MA09 | -20.2236 |
| H2018MD01 | 30.0682 |
| H2018MD02 | -31.0936 |
| H2018MD03 | -30.8964 |
| H2018MD04 | -52.3327 |
| H2018MD05 | -34.9764 |
| H2018MD06 | -26.0936 |
| H2018MD07 | -52.1318 |
| H2018MD08 | -32.1473 |
| H2018ME01 | -31.7073 |
| H2018ME02 | 7.2682 |
| H2018MI01 | 9.2218 |
| H2018MI02 | 27.9255 |
| H2018MI03 | 22.0336 |
| H2018MI04 | 26.9973 |
| H2018MI05 | -33.3936 |
| H2018MI06 | 19.2936 |
| H2018MI07 | 5.8027 |
| H2018MI08 | 14.2200 |
| H2018MI09 | -19.7045 |
| H2018MI10 | 36.1509 |
| H2018MI11 | 18.9645 |
| H2018MI12 | -34.2436 |
| H2018MI13 | -100.0000 |
| H2018MI14 | -57.4982 |
| H2018MN01 | 4.9291 |
| H2018MN02 | -0.5445 |
| H2018MN03 | 12.0864 |
| H2018MN04 | -34.1536 |
| H2018MN05 | -47.1545 |
| H2018MN06 | 38.1064 |
| H2018MN07 | 6.9627 |
| H2018MN08 | 1.3318 |
| H2018MO01 | -56.3473 |
| H2018MO02 | 23.4182 |
| H2018MO03 | 40.2373 |
| H2018MO04 | 35.0245 |
| H2018MO05 | -15.7027 |
| H2018MO06 | 37.4345 |
| H2018MO07 | 46.6318 |
| H2018MO08 | 54.6064 |
| H2018MS01 | 32.4718 |
| H2018MS02 | -100.0000 |
| H2018MS03 | 18.3191 |
| H2018MS04 | 45.3327 |
| H2018MT00 | 19.8564 |
| H2018NC01 | -41.4209 |
| H2018NC02 | 24.4373 |
| H2018NC03 | 100.0000 |
| H2018NC04 | -31.7527 |
| H2018NC05 | 23.2818 |
| H2018NC06 | 23.1155 |
| H2018NC07 | 26.4800 |
| H2018NC08 | 16.0736 |
| H2018NC09 | 19.6391 |
| H2018NC10 | 29.4127 |
| H2018NC11 | 37.1864 |
| H2018NC12 | -38.3745 |
| H2018NC13 | 3.5382 |
| H2018ND00 | 42.4055 |
| H2018NE01 | 31.7518 |
| H2018NE02 | 4.3173 |
| H2018NE03 | 52.1127 |
| H2018NH01 | 2.1800 |
| H2018NH02 | -4.2091 |
| H2018NJ01 | -27.7091 |
| H2018NJ02 | 10.0391 |
| H2018NJ03 | 11.8291 |
| H2018NJ04 | 28.4891 |
| H2018NJ05 | 2.8409 |
| H2018NJ06 | -27.8927 |
| H2018NJ07 | 11.5073 |
| H2018NJ08 | -60.2800 |
| H2018NJ09 | -42.1182 |
| H2018NJ10 | -75.5918 |
| H2018NJ11 | 17.2736 |
| H2018NJ12 | -36.1645 |
| H2018NM01 | -18.3164 |
| H2018NM02 | 10.4127 |
| H2018NM03 | -18.4518 |
| H2018NV01 | -42.4518 |
| H2018NV02 | 15.2191 |
| H2018NV03 | -0.4364 |
| H2018NV04 | -10.8673 |
| H2018NY01 | 13.4427 |
| H2018NY02 | 10.4991 |
| H2018NY03 | -13.4591 |
| H2018NY04 | -19.2436 |
| H2018NY05 | -100.0000 |
| H2018NY06 | -100.0000 |
| H2018NY07 | -100.0000 |
| H2018NY08 | -100.0000 |
| H2018NY09 | -71.5936 |
| H2018NY10 | -57.3655 |
| H2018NY11 | 17.9755 |
| H2018NY12 | -62.9582 |
| H2018NY13 | -82.6691 |
| H2018NY14 | -67.9118 |
| H2018NY15 | -83.1982 |
| H2018NY16 | -100.0000 |
| H2018NY17 | -100.0000 |
| H2018NY18 | -7.4718 |
| H2018NY19 | -1.9518 |
| H2018NY20 | -31.9564 |
| H2018NY21 | 19.5945 |
| H2018NY22 | 5.6236 |
| H2018NY23 | 15.4000 |
| H2018NY24 | 5.6118 |
| H2018NY25 | -20.2509 |
| H2018NY26 | -49.9591 |
| H2018NY27 | 29.3445 |
| H2018OH01 | 10.8836 |
| H2018OH02 | 28.2809 |
| H2018OH03 | -39.5645 |
| H2018OH04 | 27.9964 |
| H2018OH05 | 36.0618 |
| H2018OH06 | 48.4873 |
| H2018OH07 | 30.6973 |
| H2018OH08 | 33.6236 |
| H2018OH09 | -40.9436 |
| H2018OH10 | 27.3609 |
| H2018OH11 | -58.2418 |
| H2018OH12 | 30.7936 |
| H2018OH13 | -34.5827 |
| H2018OH14 | 24.5000 |
| H2018OH15 | 26.4673 |
| H2018OH16 | 21.1464 |
| H2018OK01 | 40.0345 |
| H2018OK02 | 54.5745 |
| H2018OK03 | 56.9700 |
| H2018OK04 | 40.9564 |
| H2018OK05 | 13.4773 |
| H2018OR01 | -28.6100 |
| H2018OR02 | 37.5627 |
| H2018OR03 | -100.0000 |
| H2018OR04 | -18.1473 |
| H2018OR05 | -7.8564 |
| H2018PA01 | -7.7027 |
| H2018PA02 | -52.7173 |
| H2018PA03 | -10.4955 |
| H2018PA04 | -5.1582 |
| H2018PA05 | 9.6345 |
| H2018PA06 | 2.6782 |
| H2018PA07 | 15.7891 |
| H2018PA08 | 7.1600 |
| H2018PA09 | 32.0145 |
| H2018PA10 | 31.3682 |
| H2018PA11 | 36.7445 |
| H2018PA12 | 32.4018 |
| H2018PA13 | -5.4700 |
| H2018PA14 | -5.3309 |
| H2018PA15 | 37.9745 |
| H2018PA16 | 18.0064 |
| H2018PA17 | 2.8273 |
| H2018PA18 | -90.4273 |
| H2018RI01 | -29.1282 |
| H2018RI02 | -30.7545 |
| H2018SC01 | 16.1400 |
| H2018SC02 | 25.0182 |
| H2018SC03 | 48.2245 |
| H2018SC04 | 26.5382 |
| H2018SC05 | 19.8327 |
| H2018SC06 | -47.3318 |
| H2018SC07 | 24.2818 |
| H2018SD00 | 30.0882 |
| H2018TN01 | 62.8855 |
| H2018TN02 | 42.4755 |
| H2018TN03 | 37.1945 |
| H2018TN04 | 36.1682 |
| H2018TN05 | -27.8145 |
| H2018TN06 | 49.6691 |
| H2018TN07 | 41.9536 |
| H2018TN08 | 40.9718 |
| H2018TN09 | -44.1327 |
| H2018TX01 | 56.3627 |
| H2018TX02 | 28.6182 |
| H2018TX03 | 31.8482 |
| H2018TX04 | 58.9718 |
| H2018TX05 | 37.4882 |
| H2018TX06 | 14.0882 |
| H2018TX07 | 20.1127 |
| H2018TX08 | 55.6082 |
| H2018TX09 | -100.0000 |
| H2018TX10 | 23.3545 |
| H2018TX11 | 62.2218 |
| H2018TX12 | 34.9273 |
| H2018TX13 | 63.3109 |
| H2018TX14 | 22.8400 |
| H2018TX15 | -21.9955 |
| H2018TX16 | -47.0327 |
| H2018TX17 | 24.1491 |
| H2018TX18 | -48.4364 |
| H2018TX19 | 59.4518 |
| H2018TX20 | -100.0000 |
| H2018TX21 | 26.5318 |
| H2018TX22 | 23.2409 |
| H2018TX23 | 1.5645 |
| H2018TX24 | 22.9609 |
| H2018TX25 | 22.0382 |
| H2018TX26 | 34.1909 |
| H2018TX27 | 6.0418 |
| H2018TX28 | -100.0000 |
| H2018TX29 | -43.2345 |
| H2018TX30 | -100.0000 |
| H2018TX31 | 21.7736 |
| H2018TX32 | 16.9127 |
| H2018TX33 | -48.5264 |
| H2018TX34 | -24.3336 |
| H2018TX35 | -35.2645 |
| H2018TX36 | 59.6955 |
| H2018UT01 | 40.4927 |
| H2018UT02 | 26.9800 |
| H2018UT03 | 45.2400 |
| H2018UT04 | 20.7636 |
| H2018VA01 | 16.3691 |
| H2018VA02 | 12.1964 |
| H2018VA03 | -100.0000 |
| H2018VA04 | -25.6436 |
| H2018VA05 | 9.2955 |
| H2018VA06 | 30.1718 |
| H2018VA07 | 11.6209 |
| H2018VA08 | -42.6291 |
| H2018VA09 | 44.0218 |
| H2018VA10 | 4.5100 |
| H2018VA11 | -40.9736 |
| H2018VT00 | -36.7964 |
| H2018WA01 | -12.3118 |
| H2018WA02 | -100.0000 |
| H2018WA03 | 19.0327 |
| H2018WA04 | 35.1536 |
| H2018WA05 | 15.8118 |
| H2018WA06 | -18.4873 |
| H2018WA07 | -59.7109 |
| H2018WA08 | 5.2936 |
| H2018WA09 | -100.0000 |
| H2018WA10 | -22.5145 |
| H2018WI01 | 29.3327 |
| H2018WI02 | -100.0000 |
| H2018WI03 | -10.8436 |
| H2018WI04 | -45.2700 |
| H2018WI05 | 34.0427 |
| H2018WI06 | 17.0255 |
| H2018WI07 | 25.4609 |
| H2018WI08 | 25.6518 |
| H2018WV01 | 44.8818 |
| H2018WV02 | 35.3682 |
| H2018WV03 | 54.0700 |
| H2018WY00 | 42.0009 |
Appending the original data set
APPENDING WITH PREDICTIONS
Code
#Appending original data set
results_final<-election_results%>%
mutate(R.D.Victory.Margin=
ifelse(is.na(R.D.Victory.Margin),
implementation_predictions$.pred,
R.D.Victory.Margin))DOWNLOADING CSV FILE
Code
#Downloading as csv
write.csv(results_final,"results_final.csv",row.names=FALSE)